Power Quality Monitoring in a System with Distributed and Renewable Energy Sources 63 PQ Analyzer PQ Analyzer PQ Analyzer ~ = ~ = = = ~ = = ~ ~ = = ~ Control Centre Relay Relay Relay Relay GRID Energy Container = ~ PQ Analyzer Fig. 2. Power grid with the nodes at which it is possible to combine protection functions with power quality analysis 2. Integrating power quality analysis and protection relay functions The main issue in the development of protection relay integrated with power quality analyser is the necessity to elaborate efficient algorithms for line voltage and current signals frequency spectrum determination with harmonic and interharmonic content up to 2 kHz. The mass use of power quality monitoring, postulated in the previous paragraph, demands that incorporation of power quality analysis functions into protection relay comes at a negligible additional cost to the end user. The cost of the additional hardware has thus to be as low as possible with the burden of extra functionality placed on the software. Modern microprocessor controlled protection relays employ sampling of analogue current and voltage signals and digital signal processing of the sample sequences to obtain signal Power Quality – Monitoring, Analysis and Enhancement 64 parameters like RMS, which are then used by protection algorithms. In this respect they are similar to stand alone power quality analysers that also carry out the calculations of signal parameters from sample sequences. The availability of fast and high resolution analogue to digital converters enables cost effective signal front end design suited equally well for protection relay and power quality analyser. Signal separator Fast ADC FIFO RS232 RS485 ETHERNET FLASH EPROM RAM USB DSP μP General purpose μP U n U n , I n f > f g f < f g User Interface ADC Two-state inputs Output relays Fig. 3. Block diagram of combined protection relay and power quality analyser The architecture of combined protection relay and power quality analyser has been shown in Figure 3. The architectures of power quality analyzer and protection relay are very similar. They differ only in the transient voltage surge measurement module, which has been marked by a different colour in Figure 3. While the basic signal parameterization software implemented in both devices uses Fourier techniques for spectrum determination, the protection relays contain additionally the protection algorithms software and power quality analyzers contain more elaborate spectrum analysis and statistical software. Power quality analyzers also have wider input bandwidth to measure accurately harmonic and interharmonic content of the signal up to 2 kHz. To merge protection relay and power quality analyser in a single device in a cost effective way it is necessary to employ advanced signal processing techniques like oversampling and changing the effective sampling rate in digital domain. Power Quality Monitoring in a System with Distributed and Renewable Energy Sources 65 2.1 Harmonic and interharmonic content determination 2.1.1 Introduction The international standards concerning power quality analysis (EN 50160, EN 61000-4- 7:2002, EN 61000-4-30:2003) define precisely which parameters of line voltage and current signals are to be measured and the preferred methods of measurement in order to determine power quality. In compliance with these requirements, for harmonic content determination, power quality analyzers employ sampling procedures with sampling frequency precisely synchronized to the exact multiple of line frequency. This is necessary for correct spectrum determination as is known from Fourier theory (Oppenheim & Schafer, 1998). If the sampling frequency is not equal to the exact multiple of line frequency, the spectral components present in the signal are computed with error and moreover false components appear in the spectrum. Efficient computation of signal spectrum with the use of FFT transform demands that the number of samples in the measurement interval be equal to the power of two. With sampling frequency synchronized to the multiple of line frequency, it is impossible to satisfy this requirement both in one line period measurement interval – when the measurement results are used for protection functions, and ten line periods measurement interval when interharmonic content is determined. This is the reason why some power quality analyzers available on the market offer interharmonic content measurement over 8 or 16 line periods interval. Another disadvantage of synchronizing sampling frequency to the line signal frequency is the inability to associate with each recorded signal waveform sample a precise moment in time. When the power quality meter is playing also the role of disturbance recorder, the determination of a precise time of an event is very difficult in such case. A better method to achieve the number of samples equal to the power of two both in one and ten line periods, with varying line frequency, is to use constant sampling frequency and employ digital multirate signal processing techniques. As the digital multirate signal processing involves a change in the sample rate, the sampling frequency can be chosen with the aim of simplifying the antialiasing filters that precede the analog to digital converter. According to the EN 61000-4-7:2002 standard, the signal bandwidth that has to be accurately reproduced for power quality determination is 2 kHz. The complexity of the low-pass filter preceding the A/D converter depends significantly on the distance between the highest harmonic in the signal that has to be passed with negligible attenuation (in this case the 40-th harmonic) and the frequency equal to the half of the sampling frequency. When the sampling frequency around 16 kHz is chosen, a simple 3- pole RC active filter filters can be used. 2.1.2 Multirate digital signal processing in protection relay In multirate digital signal processing (Oppenheim & Schafer, 1998) it is possible to change the sampling rate by a rational factor N/M using entirely digital methods. The input signal sampled with constant frequency f s is first interpolated by a factor of N and then decimated by a factor of M – both these processes are called collectively resampling. The output sequence consists of samples representing the input signal sampled at an effective frequency f seff , where f seff =f s ⋅(N/M) (1) The first thing that has to be determined is how many samples are needed to calculate the spectral components of the signal. For protection purposes, the knowledge of harmonics up Power Quality – Monitoring, Analysis and Enhancement 66 to 11 th used to be enough in the past. The increasing use of nonlinear loads (and competition) has led leading manufacturers of protection relays to develop devices with the ability to determine the signal spectrum up to 40 or even 50-th harmonic. Thus, taking into account that the number of samples, for computational reasons, must equal the power of two, 128 samples per period are needed (Oppenheim & Schafer, 1998). The ideal sampling frequency is then f sid = f line · 128 Hz (= 6400 Hz at 50 Hz line frequency). For power quality analysis, the EN 61000-4-7 standard demands that the measurement interval should equal ten line periods and the harmonics up to 40 th (equivalently interharmonics up to 400 th ) have to be calculated. This gives 1024 as the minimum number of samples over ten line periods meeting the condition of being equal to the power of two. The ideal sampling frequency f sid for interharmonic content determination should be equal to ((f line )/10) · 1024 Hz which is 5120 Hz at f line = 50 Hz. Knowing the needed effective sampling frequency and the actual sampling frequency f s – which for the rest of the chapter is assumed to be equal to 16 kHz, the equation (1) can be used to determine interpolation and decimation N, M values. Tables 1 and 2 gather the values of N and M (without common factors) computed from (1) for a range of line frequencies. f line 49.5 49.55 49.6 49.65 49.7 49.75 49.8 49.85 49.9 49.95 N 99 991 248 993 497 199 249 997 499 999 M 250 2500 625 2500 1250 500 625 2500 1250 2500 Table 1. Interpolation and decimation factors protection function 50.0 50.05 50.1 50.15 50.2 50.25 50.3 50.35 50.4 50.45 50.5 2 1001 501 1003 251 201 503 1007 252 1009 101 5 2500 1250 2500 625 500 1250 2500 625 2500 250 Table 1, cont. f line 49.5 49.55 49.6 49.65 49.7 49.75 49.8 49.85 49.9 49.95 N 198 991 992 993 994 199 996 997 998 999 M 625 3125 3125 3125 3125 625 3125 3125 3125 3125 Table 2. Interpolation and decimation factors for interharmoncs determination 50.0 50.05 50.1 50.15 50.2 50.25 50.3 50.35 50.4 50.45 50.5 8 1001 1002 1003 1004 201 1006 1007 1008 1009 202 25 3125 3125 3125 3125 625 3125 3125 3125 3125 625 Table 2, cont. For some line frequencies the values of N and M computed from (1) are very large, e.g. for f line = 49.991 Hz, N = 49991 and M = 125000. The computational complexity of the resampling procedure depends on how large the values of N and M are. Power Quality Monitoring in a System with Distributed and Renewable Energy Sources 67 The interpolation process consists in inserting a N-1 number of zero samples between each original signal sample pair. The resulting sample train corresponds to a signal with the bandwidth compressed with N ratio and multiplied on a frequency scale N times (Oppenheim & Schafer, 1998). To recover the original shape of the signal, the samples have to be passed through a low pass filter with the bandwidth equal to the B/N bandwidth of the signal prior to interpolation. In time domain the filter interpolates the zero samples that have been inserted between the original signal samples. 2π π π 2π 2π 2π 2π X(e jω ) 2π 2π 2π 2π π 2π 2π 2π π π π π π π π π π π H(e jω ) X HL (e jω ) X LM (e jω ) X L (e jω ) X HLM (e jω ) ω ω ω ω ω ω ( a ) ( b ) ( c ) ( d ) ( e ) (f) Fig. 4. The effect of interpolation and decimation on signal spectrum The decimation process consists in deleting M-1 samples from each consecutive group of M samples. The resulting sample train corresponds to a signal prior to the decimation but with the bandwidth expanded by a factor of M. To prevent the effect of aliasing, the sample sequence to be decimated has to be passed through a low pass filter with the bandwidth equal to 2π/M in normalized frequency. The operation of interpolation and decimation on the bandwidth of the signal has been shown in Figure 4 for N = 2 and M = 3. In this figure X(e jω ) is the spectrum of the original signal, X L (e jω ) is the spectrum of the original signal with zero samples inserted and X LM (e jω ) the spectrum of the original signal with zero samples inserted and then decimated with the M ratio. As can be seen in Figure 4 c) there is a frequency aliasing. If the signal after interpolation is passed through a low pass filter with suitable frequency characteristic H(e jω ) the effect of frequency aliasing is avoided as shown in Figure 4 d). To preserve as much of the bandwidth of the original signal as possible, the low pass filter used in the resampling process has to have a steep transition between a pass and stop bands. The complexity of the filter depends heavily on the magnitude of the greater of the values of N and M. This is one of the many reasons why the values of N and M should be Power Quality – Monitoring, Analysis and Enhancement 68 chosen as low as possible but at the same time the f eff computed from (1) should be as close as is necessary to the ideal sampling frequency f sid . The accuracy with which f eff is to approximate f sid could be determined from simulating how different values of N and M affect the accuracy of spectrum determination. However some clues about the values of N and M can be obtained from EN 61000-4-7 standard. In chapter 4.4.1 it states that the time interval between the rising edge of the first sample in the measurement interval (200 ms in 50 Hz systems) and the rising edge of the first sample in the next measurement interval should equal 10 line periods with relative accuracy not worse than 0.03%. Therefore, for each line frequency, the values of N and M should be chosen so as the relative difference E eff between the ideal sampling frequency f sid , and the effective sampling frequency f eff meets the following condition () seff sid eff sid ff E f 0.003 ⋅ =≤ (2) The frequency characteristic of the low pass filter used in the resampling procedure depends on the values of N and M. If a different filter is used for each N, M pair it places a heavy burden on limited resources of DSP processor system in a protection relay. A solution to this problem is to fix the value of N and choose M according to the following formula s line Nf MRound f SN L   ⋅  =   ⋅     (3) where L is the number of periods used in the spectrum determination and SN is the number of the samples in L periods (128 samples per one period for protection functions, 1024 samples per 10 periods for power quality analysis). The Round(x) function gives the integer closest to x. The low pass filter is then designed with the bandwidth equal to 2π/M min where M min is the value of M computed from (2) for highest line frequency f line . For power quality analysis when the interharmonics content has to be determined, N=600, the minimum value of M is 1630 at f line = 57.5 Hz, the maximum value of M is 2206 at f line = 42.5 Hz. The maximum absolute value of E eff is equal to 0.03% and the effective sampling frequency is within the range recommended by EN 61000-4-7 standard. As the error of spectrum determination increases with increasing E eff it is sufficient to carry out the analysis of the accuracy of spectrum determination for line frequency, for which the E eff is largest. The obtained accuracy should then be compared with the accuracy of spectrum determination when the sampling frequency is synchronized to the multiple of the same line frequency with the error of 0.03%. For the analysis a signal composed of the fundamental component, 399 interharmonic with 0.1 amplitude relative to the fundamental, 400 interharmonic with 0.05 amplitude relative to the fundamental and 401 interharmonic with 0.02 amplitude relative to the fundamental should be selected. This is the worst case signal because on the one hand the error is greatest at the upper limit of the frequency range, and on the other hand when close interharmonics are present, there is leakeage from the strongest interharmonic to the others. Figure 5 shows the spectrum of the test signal determined when the synchronization technique is used and Figure 6 shows the spectrum when the digital resampling technique is used. In both cases the resulting sample rate is identical. Power Quality Monitoring in a System with Distributed and Renewable Energy Sources 69 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 0 50 100 150 200 250 300 350 400 450 500 n |h(n)/h(11)| 1st harmonic 399th interharmonic 40th harmonic 1.47% of 1st h 401st interharmonic 0.775% of 1st h Fig. 5. Spectrum of the test signal when synchronization of the sampling frequency to the multiple of line frequency is used 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 0 50 100 150 200 250 300 350 400 450 500 n |h(n)/h(11)| 1st harmonic 399th interharmonic 40th harmonic 1.48% of 1st h 401st interharmonic 0.78% of 1st h Fig. 6. Spectrum of the test signal when resampling technique is used Power Quality – Monitoring, Analysis and Enhancement 70 The two spectra are almost identical and they both give the same error in the interharmonic level determination. The level of 399 th interharmonic is very close to the true value. However the level of 40 th harmonic is almost three times higher than the true value and the level of 401 st interharmonic is almost four times higher than the true value. The observed effect can be explained by leakage of the spectrum from 399 th interharmonic of relatively large level to neighbouring interharmonics (Bollen & Gu 2006). The detailed analysis carried out for the whole range of line frequency and various signal composition shows that if the error between the ideal sample rate and actual sample rate at the input of Fourier spectrum computing procedure is the same, both methods give equally accurate results. For the protection functions the needed frequency resolution is ten times lower than for interharmonic levels determination. It suggests, that the values of N and M can be chosen such that the maximum value of E eff < 0.3%. With N=80, the minimum value of M equal to 174 at f line = 57.5 Hz, and the maximum value of M equal to 235 at f line = 42.5 Hz, the maximum absolute value of E eff is equal to 0.284%. The detailed analysis shows that harmonics are determined with the accuracy which is better than 1%. 3. New input circuits used for parameters determination of line current and voltage signals The measurement of line voltage and current signals for power quality analysis demands much higher accuracy than is needed for protection purposes. Traditional voltage and current transducers used in primary circuits of power stations cannot meet the requirements of increased accuracy and wide measurement bandwidth. New types of voltage and current transducers are needed with frequency measurement range equal to at least the 40-th harmonic of fundamental frequency, high dynamic range and very good linearity. For current measurements Rogowski coils may be used. They have been used for many years in applications requiring measurements of large current in wide frequency bandwidth. The traditional technologies used for making such coils were characterized by large man labour. Research work has been carried out at many laboratories to develop innovative technologies for Rogowski coil manufacture. These technologies are based on multilayer PCB. 3.1 Principle of PCB Rogowski coil construction The principle of Rogowski coil operation is well known (http://www.axilane.com/PDF_Files/Rocoil_Pr7o.pdf). The basic design consists in winding a number of turns of a wire on a non-magnetic core, Figure 7. The role of the core is only to support mechanically the windings. The voltage V(t) induced at the terminations is expressed by the following equation () ddI Vt nA dt dt 0 Φ =− =− ⋅ ⋅ ⋅ μ (4) where µ 0 is the magnetic permeability of the vacuum, n is the number of turns, A is the area of the single turn (referring to Figure 7, A=π·r 2 ) and I is the current flowing in the conductor coming through the coil. Power Quality Monitoring in a System with Distributed and Renewable Energy Sources 71 Fig. 7. A simplified construction of the Rogowski coil The most important parameter of the Rogowski coil is its sensitivity S. It is the ratio of the RMS value of the voltage at coil terminations to the RMS value of the sine current flowing in the wire going through the centre of the coil. Because of the factor dI/dt in equation (4), the sensitivity is directly proportional to the frequency of the current signal. In applications of the Rogowski coil in the power industry sector the sensitivity is given at the fundamental line frequency 50 Hz or 60 Hz. The sensitivity of the coil which has the shape as shown in Figure 7, is given by the following formula: 0 2 ef n SA R μω =⋅ ⋅⋅ ⋅π⋅ (5) where n is the total number of the turns, ω is the angular pulsation of the sinusoidal current I, and R is the radius of the coil. The factor A has been replaced with A ef because in practice not every turn has to have the same dimensions. The last factor in the equation (5) shows that the sensitivity of the coil is directly proportional to the density l of the turns where l=n/(2·π·R). The PCB design of the Rogowski coil replaces the wire turns by induction coils printed on multilayer boards. On each layer there is a basic coil in the form of a spiral. The coils on neighboring layers are connected by vias. The vias can be buried or through. The buried vias leave more board space for the coil but are much more expensive to manufacture. A design of the first 4 layers of 16-layer board with buried vias is presented in Figure 8. Photos of the multilayer board designs with through and buried vias are presented in Figures 9 a) and 9 b) respectively. [...]... centers aligned, Figure 14 and as close to each other as possible thus forming a sandwich with two secondary winding boards between every primary winding board 4 References Ackerman T ed (2005) Wind Power in Power Systems, ISBN 0 -47 0-85508-8, John Wiley & Sons, England 76 Power Quality – Monitoring, Analysis and Enhancement Bollen, M H J & Gu I (2006) Signal Processing of Power Quality Disturbances, IEEE... Press, ISBN-13 978-0 -47 1-73168-9, USA European Standard EN 50160: Voltage characteristics of electricity supplied by public distribution systems European Standard EN 61000 -4- 30:2003: Electromagnetic compatibility (EMC) Part 4- 30: Testing and measurement techniques – Power quality measurement methods European Standard EN 61000 -4- 7:2002: Electromagnetic compatibility (EMC) Part 4- 7: Testing and measurement... surprisingly, standards have been introduced to cover this field They define the types and sizes of disturbance, and the tolerance of various types of equipment to the possible 78 Power Quality – Monitoring, Analysis and Enhancement disturbances that may be encountered The principal standards in this field are IEC 61000, EN 50160, and IEEE 1159 Standards are essential for manufacturers and users alike,... in Power Quality Monitoring Zahra Moravej1, Mohammad Pazoki2 and Ali Akbar Abdoos3 3Electric 1 2Electric and computer engineering faculty, Semnan University, and computer engineering faculty, Babol Noshirvani University of Technology Iran 1 Introduction The definition of power quality according to the Institute of Electrical and Electronics Engineers (IEEE) dictionary [159, page 807] is that power quality. .. applied to first three IMF extracted from EMD to assess instantaneous amplitude and phase which are then employed for feature vector construction The pattern recognition system used PNN classifier for identification the various power quality events 86 Power Quality – Monitoring, Analysis and Enhancement Nine types of power quality disturbances are generated in MATLAB with a sampling frequency 3.2 kHz... frequencies, where the window is narrow and time resolution is good in any case, a more symmetrical window should be used At low frequencies, where the window is wider and frequency resolution is less critical, a more asymmetrical window may be used to prevent the event from appearing too far ahead on the S-transform This 84 Power Quality – Monitoring, Analysis and Enhancement concept led us to design... concept of powering and grounding sensitive equipment in a matter that is suitable to the operation of that equipment.” In recent years, power quality (PQ) has become a significant issue for both utilities and customers PQ issues and the resulting problems are the consequences of the increasing use of solid-state switching devices, non-linear and power electronically switched loads, unbalanced power systems,... b2 are the dimensions of the mosaic as shown in Figure 12  74 Power Quality – Monitoring, Analysis and Enhancement a1 a a2 b2 b b1 Fig 12 A simplified printed circuit coil The upper bound on n in equation (6) is determined by the minimal track thickness Practical experience shows that the sensitivity of the coil computed using equations (5) and (6) is within 10% of the coil sensitivity obtained from... phenomena in critical process loads Effects on equipment and process operations can include malfunctions, damage, process disruption and other anomalies (Baggini 2010; IEEE 1995) Troubleshooting these problems requires measuring and analyzing power quality and that leads us to the importance of monitoring instruments in order to localize the problems and find solutions The critical stage for any pattern... discrete STFT than dyadic wavelet and Binary-Tree Wavelet Filters (BT-WF) for voltage disturbance analysis Application of Signal Processing in Power Quality Monitoring 81 4. 2 Discrete Wavelet Transform (DWT) Wavelet-based techniques are powerful mathematical tools for digital signal processing, and have become more and more popular since the 1980s It finds applications in different areas of engineering due . 50 .4 50 .45 50.5 2 1001 501 1003 251 201 503 1007 252 1009 101 5 2500 1250 2500 625 500 1250 2500 625 2500 250 Table 1, cont. f line 49 .5 49 .55 49 .6 49 .65 49 .7 49 .75 49 .8 49 .85 49 .9 49 .95. in Power Systems, ISBN 0 -47 0-85508-8, John Wiley & Sons, England Power Quality – Monitoring, Analysis and Enhancement 76 Bollen, M. H. J. & Gu I. (2006) Signal Processing of Power. software and power quality analyzers contain more elaborate spectrum analysis and statistical software. Power quality analyzers also have wider input bandwidth to measure accurately harmonic and